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Optimizations for High Performance Network Virtualization

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Abstract

The increasing requirements of intensive interoperaterbility among the distributed nodes desiderate the high performance network connections, owing to the substantial growth of cloud computing and datacenters. Network I/O virtualization aggregates the network resource and separates it into manageable parts for particular servers or devices, which provides effective consolidation and elastic management with high agility, flexibility and scalability as well as reduced cost and cabling. However, both network I/O virtualization aggregation and the increasing network speed incur higher traffic density, which generates a heavy system stress for I/O data moving and I/O event processing. Consequently, many researchers have dedicated to enhancing the system performance and alleviating the system overhead for high performance networking virtualization. This paper first elaborates the mainstreaming I/O virtualization methodologies, including device emulation, split-driver model and hardware assisted model. Then, the paper discusses and compares their specific advantages in addition to performance bottlenecks in practical utilities. This paper mainly focuses on the comprehensive survey of stateof-the-art approaches for performance optimizations and improvements as well as the portability management for network I/O virtualization. The approaches include various novel data delivery schemes, overhead mitigations for interrupt processing and adequate resource allocations for dynamic network states. Finally, we highlight the diversity of I/O virtualization besides the performance improvements in network virtualization infrastructure.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, Shanghai, 200240, China

    Fan-Fu Zhou, Ru-Hui Ma, Jian Li, Li-Xia Chen, Wei-Dong Qiu & Hai-Bing Guan

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  1. Fan-Fu Zhou
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  2. Ru-Hui Ma
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Correspondence to Hai-Bing Guan.

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Zhou, FF., Ma, RH., Li, J. et al. Optimizations for High Performance Network Virtualization. J. Comput. Sci. Technol. 31, 107–116 (2016). https://doi.org/10.1007/s11390-016-1614-x

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